Evolution of library data sets

ABSTRACT

An optical metrology includes a library, a metrology tool and a library evolution tool. The library is generated to include a series of predicted measurements. Each predicted measurement is intended to match the measurements that a metrology device would record when analyzing a corresponding physical structure. The metrology tool compares its empirical measurements to the predicted measurements in the library. If a match is found, the metrology tool extracts a description of the corresponding physical structure from the library. The library evolution tool operates to improve the efficiency of the library. To make these improvements, the library evolution tool statistically analyzes the usage pattern of the library. Based on this analysis, the library evolution tool increases the resolution of commonly used portions of the library. The library evolution tool may also optionally reduce the resolution of less used portions of the library.

CLAIM OF PRIORITY

The present application is a continuation of U.S. patent applicationSer. No. 10/145,848, entitled “EVOLUTION OF LIBRARY DATA SETS,” filedMay 14, 2002 now U.S. Pat. No. 6,898,596, which claims priority to U.S.Provisional Patent Application Ser. No. 60/346,252, filed Oct. 23, 2001and Ser. No. 60/351,494, filed Jan. 24, 2002, all of which are herebyincorporated herein by reference.

TECHNICAL FIELD OF THE INVENTION

The subject invention relates to the use of data sets or libraries tofacilitate the analysis of experimental samples. In particular, anapproach is disclosed that improves the speed, versatility andefficiency of libraries used for this purpose.

BACKGROUND

Over the past several years, there has been considerable interest inusing optical scatterometry (i.e., optical diffraction) to performcritical dimension (CD) measurements of the lines and structuresincluded in integrated circuits. Optical scatterometry has been used toanalyze periodic two-dimensional structures (e.g., line gratings) aswell as three-dimensional structures (e.g., patterns of vias or mesas).Scatterometry is also used to perform overlay registration measurements.Overlay measurements attempt to measure the degree of alignment betweensuccessive lithographic mask layers.

Various optical techniques have been used to perform opticalscatterometry. These techniques include broadband scatterometry (U.S.Pat. Nos. 5,607,800; 5,867,276 and 5,963,329), spectral ellipsometry(U.S. Pat. No. 5,739,909) as well as spectral and single-wavelength beamprofile reflectance and beam profile ellipsometry (co-pendingapplication Ser. No. 09/818,703 filed Mar. 27, 2001). In addition it maybe possible to employ single-wavelength laser BPR or BPE to obtain CDmeasurements on isolated lines or isolated vias and mesas.

Most scatterometry systems use a modeling approach to transformscatterometry signals into critical dimension measurements. For thistype of approach, a theoretical model is defined for each physicalstructure that will be analyzed. The theoretical model predicts theempirical measurements (scatterometry signals) that scatterometrysystems would record for the structure. A rigorous coupled wave theorycan be used for this calculation. The theoretical results of thiscalculation are then compared to the measured data (actually, thenormalized data). To the extent the results do not match, thetheoretical model is modified and the theoretical data is calculatedonce again and compared to the empirical measurements. This process isrepeated iteratively until the correspondence between the calculatedtheoretical data and the empirical measurements reaches an acceptablelevel of fitness. At this point, the characteristics of the theoreticalmodel and the physical structure should be very similar.

The calculations discussed above are relatively complex even for simplemodels. As the models become more complex (particularly as the profilesof the walls of the features become more complex) the calculationsbecome exceedingly long and complex. Even with high-speed processors,the art has not developed a suitable approach for analyzing more complexstructures to a highly detailed level on a real time basis. Analysis ona real time basis is very desirable so that manufacturers canimmediately determine when a process is not operating correctly. Theneed is becoming more acute as the industry moves towards integratedmetrology solutions wherein the metrology hardware is integrateddirectly with the process hardware.

One approach that allows a manufacturer to characterize features in realtime is to create “libraries” of predicted measurements. This type ofapproach is discussed in PCT application WO 99/45340, published Sep. 10,1999 as well as the references cited therein. In this approach, thetheoretical model is parameterized to allow the characteristics of thephysical structure to be varied. The parameters are varied over apredetermined range and the theoretical result for each variation to thephysical structure is calculated to define a library of solutions. Whenthe empirical measurements are obtained, the library is searched to findthe best fit.

In general, libraries have proven to be an effective method for quicklyanalyzing samples. Unfortunately, libraries have also proven to havetheir own disadvantages. One disadvantage results from the fact thatlibraries must be generated in a reasonable amount of time and mustoccupy a reasonable amount of space. This means that libraries must havelimited range (i.e., the library is limited to a portion of the totalsolution space). Libraries must also have limited resolution (i.e.,there must be some granularity between solutions). These limitationsbecome problematic when test data doesn't closely match the range andresolution of the library being used. If a library has inadequate range,for example, test data may not match any of the library's storedsolutions. This same result can occur when a library has adequate range,but the range is incorrectly centered in the spectrum of solutions.Libraries may also have inadequate resolution causing test data to fallbetween stored solutions. In other cases, libraries may have excessiverange or resolution wasting both time and space.

One approach for dealing with this problem is to use the library valuesas a starting point for the solution and then determine parameters usinginterpolation or estimation procedures. U.S. Pat. No. 5,867,276describes a system of training a library to permit linear estimations ofsolutions. Another form of interpolation can be found in U.S. PatentApplication 2002/0038196, published Mar. 28, 2002. PCT WO 02/27288,published Apr. 4, 2002 suggests using a coarse library and a real timeregression approach to improve results. The latter documents areincorporated by reference.

Even using the above approaches, the initial libraries in workingoptical metrology systems are seldom optimal for either range orresolution. This follows because optimal values for range and resolutionare difficult to predict as libraries are being built. Inevitable errorsin these predictions mean that libraries are never entirely efficient atanalyzing test results. Errors of this type often compound, as librariesare used and operational parameters change or drift. In these cases,libraries become increasingly out of sync with their optical metrologysystems and increasingly inefficient at analyzing test results. A moreideal solution would be to develop a system that adapted libraries tothe actual test results generated by optical metrology systems.

BRIEF SUMMARY

An aspect of the present invention provides a library evolution methodfor use with optical metrology systems. Systems of this type use alibrary for each physical structure that will be analyzed. The libraryfor each structure is based on a corresponding parametric model. Theparametric model predicts the empirical measurements that a metrologysystem would record for the structure. The parameters allow the model tobe varied or perturbed, to produce a series of predicted measurementsets. Each library contains a series of predicted measurements sets,each set corresponding to a particular set of model parameters.

The underlying parametric model may be used to predict empiricalmeasurements that are associated with a wide range of attributes withinthe physical structure being modeled. In semiconductor wafers,two-dimensional structures (e.g., line gratings) as well asthree-dimensional structures (e.g., patterns of vias or mesas) are oftenmodeled. The structures may be modeled as parts of a surface layer or asparts of subsurface layers. Models may also account for layerproperties, such as transparency, thickness and type for both surfaceand subsurface layers. In some cases, alignment between different layersmay also be modeled.

As the optical metrology system operates, its empirical measurements arecompared to the predicted measurement sets stored in the library. If amatch is found, the parameters used to generate the matching set ofpredicted measurements are assumed to describe the physical structurebeing analyzed. In the best case, the process of library searchingresults in matches most, if not all of the time. This results when thelibrary has been constructed to have the correct range and resolution.Range, in this context, means that the predicted measurement sets in thelibrary span the range of empirical measurements that are encounteredempirically. Resolution means that the granularity of predictedmeasurement sets within the library is fine enough that close matchesmay be found for the empirical measurements that are encounteredempirically. In real-world systems, where computational and storageresources are limited, range and resolution of a given library must belimited.

The library evolution method dynamically optimizes the range andresolution of a library to correspond to the empirical measurements thatare encountered empirically. Optimization may be applied to a library asinitially created or to a previously optimized library. To optimize alibrary, the evolution method monitors the library's use. As the libraryis used a usage pattern is generated. The usage pattern identifies theportions of the library that are heavily used along with the portionsthat are less used or unused.

A library evolution program reorganizes the library based on the usagepattern. The library program generates new predicted measurement sets inportions of the library where additional resolution or range would bebeneficial. Optionally, the library evolution program may also deletepredicted measurement sets to reduce unneeded range or resolution. Theoverall effect is to transform the library to have range and resolutionthat matches the actual use of the library. This process may beperformed continuously, in parallel with the use of the library, orperformed as an offline process at periodic intervals.

It should also be appreciated that the library evolution method may beapplied to a wide range of systems and is not limited to use withinoptical metrology systems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the software components used by an opticalmetrology system using the library evolution method of the presentinvention.

FIG. 2 is a block diagram of a simplified library shown as a target forthe library evolution method of the present invention.

FIG. 3 is an example of a usage pattern that might correspond to thelibrary of FIG. 2.

FIG. 4 is a block diagram of a library data set evolved from the librarydata set of FIG. 2 after the addition of new predicted measurement setsbased on the usage pattern of FIG. 3.

FIG. 5 is a block diagram of a library data set evolved from the librarydata set of FIG. 4 after the removal of predicted measurement sets basedon the usage pattern of FIG. 3.

FIG. 6 is a block diagram of a library data set evolved from the librarydata set of FIG. 2 after a second possible enhancement based on theusage pattern of FIG. 3.

FIG. 7 is a block diagram showing use of a vector to record the usage ofthe library of FIG. 2.

FIG. 8 is a block diagram showing the vector of FIG. 7 after use of thelibrary for a statistically significant time period.

FIG. 9 is a block diagram showing reorganization of the library of FIG.2 based on the usage vector of FIG. 8.

FIG. 10 is a block diagram showing a representative networked deploymentof the present invention.

FIG. 11 is a functional chart showing fault tolerant operation of thepresent invention.

DETAILED DESCRIPTION

An aspect of the present invention provides a method for improving thespeed, accuracy and versatility of programs that use libraries as partof their problem solving strategies. To describe this method, FIG. 1shows a representative use of the present invention as part of anoptical metrology system 100. As previously described, systems of thistype are typically used to inspect semiconductor wafers by analyzingperiodic two-dimensional structures (e.g., line gratings) as well asthree-dimensional structures (e.g., patterns of vias or mesas). Overlayregistration measurements may also be performed to quantify the degreeof alignment between successive lithographic mask layers.

As shown in FIG. 1, optical metrology system 100 includes a metrologytool 102, an analysis program 104, an evolution program 108 and alibrary 106. Metrology tool 102 is representative of the wide range oftools of this nature. For this particular example, metrology tool 102may be assumed to be one of systems available from Therma-Wave Inc.Analysis program 104 controls the operation of metrology tool 102 andinterprets its empirical measurements.

Library 106 is created by modeling one or more physical structures. Forthe modeling process, each physical structure is described using acorresponding parametric model. The parametric model predicts theempirical measurements that metrology tool 102 would record for thecorresponding physical structure. The parameters allow the model to bevaried or perturbed, to create a series of similar physical structuresand a corresponding series of predicted measurement sets. Library 106contains a series of predicted measurements sets generated in thisfashion. Library 106 also contains the parameters used to generate thepredicted measurements sets. Within library 106, each set of predictedmeasurements is associated with the parameters used during itsgeneration.

The underlying parametric model may be used to predict empiricalmeasurements that are associated with a wide range of attributes withinthe physical structure being modeled. In semiconductor wafers,two-dimensional structures (e.g., line gratings) as well asthree-dimensional structures (e.g., patterns of vias or mesas) are oftenmodeled. The structures may be modeled as parts of a surface layer or asparts of subsurface layers. Models may also account for layerproperties, such as transparency, thickness and type for both surfaceand subsurface layers. In some cases, alignment between different layersmay also be modeled.

After metrology tool 102 has inspected a sample, analysis program 104compares the resulting empirical measurements to the predictedmeasurement sets stored in library 106. If a match is found, theparameters used to generate the matching set of predicted measurementsare assumed to describe the physical structure being analyzed. In thebest case, the process of searching library 106 results in matches most,if not all of the time. This results when library 106 has beenconstructed to have an optimal range and resolution. Range, in thiscontext, means that the predicted measurement sets in library 106 spanthe range of empirical measurements that are encountered empirically.Resolution means that the granularity of predicted measurement setswithin library 106 is fine enough that close matches may be found forthe empirical measurements that are encountered empirically. Inreal-world systems, where computational and storage resources arelimited, both the range and resolution of library 106 must be limited.

Evolution program 108 dynamically optimizes the range and resolution oflibrary 106 to correspond to the empirical measurements that areencountered empirically by optical metrology tool 102. Optimization maybe applied to library 106 as initially created or at any timethereafter. To perform this optimization, analysis program 104 monitorsthe use of library 106. As library 106 is used a usage pattern isgenerated. The usage pattern identifies the portions of the library 106that are heavily used along with the portions that are less used orunused. Based on the usage pattern, evolution program 108 generates newpredicted measurement sets in portions of library 106 where additionalresolution or range would be beneficial. Optionally, evolution program108 may also delete predicted measurement sets to reduce unneeded rangeor resolution within less used portions of library 106. The overalleffect is to transform library 106 to have range and resolution thatmatch the empirical measurements actually encountered by opticalmetrology tool 102.

To better describe the evolution process, FIG. 2 shows a simplifiedversion of library 106. In FIG. 2, library 106 includes a series of onehundred twenty (120) predicted measurements, evenly distributed withinthe range of zero to twelve. The resolution within library 106 is tenpredicted measurement sets per unit of range.

FIG. 3 shows a hypothetical usage pattern for library 106 of FIG. 2. Theusage pattern is a statistical record of the searches performed onlibrary 106. This includes both successful and unsuccessful searches andincludes searches that fall within or outside of the current range oflibrary 106. As shown in the example usage pattern of FIG. 3, two-thirdsof library 106 is unused. The remaining portions of library 106 are moreheavily used with the greatest used restricted to a mere one-sixth oflibrary 106. In general, it should be appreciated that the usage patterngenerated for library 106 includes both successful and unsuccessfulsearches both inside and outside of the range of library 106.

To make library 106 more closely match the usage pattern of FIG. 3,evolution program 108 generates new predicted measurement sets withinthe most used portions of library 106. This is shown in FIG. 4 whereevolution program 108 has generated twenty new predicted measurementsets. As a result, the most commonly used portion of library 106 now hasthe greatest number of predicted measurement sets and the highestresolution.

Optionally, evolution program 108 may also prune the regions of library106 that are least used. This is shown in FIG. 5 where evolution program108 has removed twenty of the predicted measurement sets within theleast used regions of library 106. As a result of the enhancement andpruning operations, the most commonly used portion of library 106 nowhas the greatest number of predicted measurements. The least usedportions of library 106 have the smallest number of predictedmeasurements. The overall result is that library 106 includes threedistinct levels of resolution. The outer regions, which receive theleast use, contain the smallest number of predicted measurements. Anintermediate region includes more predicted measurements and the inner,most-heavily used region includes the most predicted measurements. Thisclosely approximates the pattern of use shown in FIG. 3. Of course, aneven more aggressive reorganization could have been performed using thesame basic method.

Several methods exist for identifying portions of library 106 forpruning or enhancement. One method is to statistically evaluate theusage of library 106. The statistical evaluation identifies mean andstandard deviations for the usage pattern. Evolution program 108 thenrepopulates library 106 so that library density increases in regionsclosest to the mean value and decreases at successively greater standarddeviations from the mean. FIG. 6 can be used to illustrate this type ofreorganization if it is assumed that the mean value is six and thestandard deviation is two. Within that figure, the region within onestandard deviation (i.e., four through eight) has a total of sixty-fourpredicted measurement sets. The region within two standard deviations(i.e., two through four and eight through ten) has approximately half asmany predicted measurements (in this case, thirty-two). The regionwithin three standard deviations (i.e., zero through two and ten throughtwelve) has approximately half again as many predicted measurements (inthis case, sixteen). The samples are therefore, distributed using apower of two distribution where each more distant region (standarddeviation) has half of the sample population as the preceding region.

The standard deviation based reorganization is beneficial because itautomatically adapts to perform library annealing and diffusing (i.e.,increases or decreases in library density to accommodate different usagepatterns) as well as library centering (i.e., shifts in the rangecovered by the library).

Another approach is to configure library 106 to maintain usage countsfor sub-ranges within library 106. The sub-ranges can be created withany desired granularity. FIG. 7 shows a representative implementationwhere each sub-range covers one range unit (e.g., 0 to 1, 1 to 2, 2 to 3and so on). A vector of usage counters tracks the number of searcheswithin a particular sub-range. As shown in FIG. 7, the usage counts areinitially set to zero. Each counter is incremented each time a search isperformed within its associated sub-range. FIG. 8 continues this exampleto a point in time where the incrementing process has been repeated astatistically significant number of times. As shown in FIG. 8, the usagecounts that correspond to the region between two and four are highest.The usage counts for the regions one to two and four to five are nexthighest.

The usage counts for the regions zero to one and five to six are nexthighest. The remaining usage counts are zero. As indicated by the vectorof usage counts, library 106 (for this example) is suboptimal both forrange and resolution. Only a small portion of library 106 is used. Inaddition, it may be assumed that searches are performed beyond the rangeof library 106.

FIG. 9 illustrates redistribution of library 106 by evolution program108 based on the usage vector of FIG. 8. As shown in FIG. 9, evolutionprogram 108 has selectively pruned and augmented library 106 to matchthe usage vector. The most heavily used portions of library 106 now havethe highest resolution. The least used regions have the lowestresolution. The library has been effectively shifted to center its rangearound its most searched sub-ranges. The usage vector of library 106 hasalso been reinitialized so all usage counts are zero. The process oflibrary use (with the usage recording vector a new usage pattern)followed by analysis and optimization by evolution program 108 can berepeated any number of times.

The usage vector approach is beneficial because it automatically adaptsto perform library annealing and diffusing (i.e., increases or decreasesin library density to accommodate different usage patterns) as well aslibrary centering (i.e., shifts in the range covered by the library).The usage vector approach also adapts to arbitrary usage patterns thatmight be difficult to accommodate using other approaches.

It should be noted that usage is not the only factor that is relevantwhen evolving library 106. For example, it could be the case thatdifferent predicted measurements within library 106 have differentassociated values. This could occur when several different methods areused to generate predicted measurements with some of the methods beingmore costly or time consuming that other methods. In this sort of case,evolution program can be configured to account for additional factors aspart of the pruning and enhancement process. Entries within the usagevector could be marked with a special “do not delete” value wherecertain predicted measurements should be maintained indefinitely. Theusage vector can also be augmented to include a value entry for eachpredicted measurement. Each value entry would be initialized to includethe value of the corresponding predicted measurement allowing evolutionprogram 106 to account for value when choosing which predictedmeasurements to prune.

It should be noted that the steps of evolving library 106 may includegenetic algorithms to improve or increase the population of predictedmeasurements. The use of genetic algorithms in optical metrology isdescribed in U.S. Pat. No. 5,864,633 as well as in PCT WO 01/75425, bothincorporated herein by reference.

Software Architecture

The evolution method may be implemented using a wide range of differentsoftware architectures. For the architecture shown in FIG. 1, evolutionprogram 108 and metrology tool 102 coexist on a single system (orcluster). Evolution program 108 works as a parallel background processto improve library 106 while metrology tool 102 is being used to analyzeempirical measurements. For a second architecture, shown in FIG. 10metrology tool 102, evolution program 108 and library 106 operate in anetworked environment. Within this environment, metrology tool 102,evolution program 108 and library 106 are hosted on one or more separatecomputer systems. Operation of metrology tool 102 remotely fromevolution program 108 increases the throughput of both programs sincethey no longer compete for the same system resources.

Remote operation has other advantages as well. As shown in FIG. 10,evolution program 108 and library 106 may be shared between metrologytool 102 and one or more different metrology tools (shown as metrologytools 102 b, 102 c and 102 d). The networking of these variouscomponents allows a single library 106 and a single evolution program108 to service a range of different tools.

Metrology tools 102 may also be configured to have local libraries.These local libraries are maintained by evolution program 108 in thesame fashion as library 106. The local libraries may be configured forexclusive use by their associated metrology tool 102 or for shared useby one or more remote metrology tools 102 (e.g, the local library ofmetrology tool 102 b can be shared between metrology tools 102 a, 102 band 102 c). Metrology tools 102 may use a selection process to select alocal library for use. This allows metrology tools 102 to choose analternate library and continue operation in cases where a currently usedlibrary becomes ineffective.

The local libraries may be configured to operate in place of or tosupplement library 106. In cases where the local libraries supplementlibrary 106, evolution program 108 may populate each local library toselectively enhance the areas of library 106 that are most used by thecorresponding metrology tool 10. In these cases, each local library isevolved to act as a cache for the predicted results most used by theassociated metrology tool 102. Metrology tools 102 would access library106 only for less used predicted results.

Fault-Tolerant Library Evolution

In most applications, metrology tool 102, evolution program 108 andlibrary 106 will be used as part of a production process. The importanceof maintaining production on a continuous basis is often paramount. As aresult, it is important that evolution program 108 operate in faulttolerant manner. A method 1100 for fault tolerant operation is shown inFIG. 11. For this method, library 106 is initially generated using thepreviously described modeling process (see step 1102). Once generated,library 106 is installed for use.

As library 106 is used, its performance is monitored (see steps 1104 and1106). Typically, this is done by a monitoring process or thread and maybe done on a periodic or continuous basis. As long as the performance oflibrary 106 meets preset criteria, it will continue to be used.

The alternative (see step 1108) occurs when the library performance hasbecome unacceptable. In this case, a determination is made as to whetherthe use of library 106 is trending out. In this context, trending outmeans that the use of library 106 has changed in some way that isaddressable by evolution program 108. As discussed previously, thiswould include cases where library 106 is fixable by changes in librarydensity in a given area (library annealing and diffusing) or changed indata ranges (library centering). If the use of library 106 is trendingout, modification of library 106 is undertaken by evolution program 108(step 1110). When complete, the modified library 106 is installed foruse in step 1112.

The alternative to steps 1110 and 1112 occurs when the use of library106 has exited the solution space of library 106. As compared totrending out, changes of this type are more severe and are not generallyaddressable by evolution program 108. In cases of this type, the use ofthe library is halted and the modeling tool is used to generate resultsin real time to match the empirical readings measured during theproduction process (step 1114). This allows production to continue whilea new library 106 is created.

Each time a new library is evolved, its production is evaluated. If itis found to be unstable, it may be replaced with the previous libraryand the evolution process restarted. This provides a fault tolerantapproach to library evolution. This sort of fault tolerant operationnaturally involves comparing the performance of a newly generatedlibrary with the previously used version of the same library. In orderto avoid anomalous results during this comparison, it is generallyuseful to require that the evolution of the new library be completed toa sufficient degree before comparison is made. For example,implementations might require that new versions include a fixedpercentage of new predicted measurements before comparison is made. Thisavoids the situation where an evolved, but highly similar library isactually worse at solving a given set of problems.

Concurrent Library Evolution

The division of tasks between evolution program 108 and the remainder ofthe components of metrology system 100 means that the evolution processmay continue, even as metrology system 100 remains in use. To supportconcurrent evolution, evolution program 108 may be configured to operateon selected portions of library 106. Evolution program 108 optimizeseach selected portion as metrology system 100 continues to use theoriginal version of library 106. When the optimization of a selectedportion is complete, evolution program inserts it into library 106. Thismay be performed using a fault tolerant transaction allowing theoptimization to be undone if it turns out to be undesirable in practice.

Alternate Applications

The previous description has focused on the use of library evolutionwithin the context of optical metrology. The use of library evolution iswell suited to this context because of the extreme difficulty associatedwith creating optimal libraries for optical metrology processes. Ingeneral, it should be appreciated that these same difficulties may beencountered whenever a library having finite resolution and range isused to characterize an infinite solution space. As a result, thelibrary evolution method described above has general applicability tosolve a wide range of different problems. Genomic mapping is one casewhere a library of solutions may be used to analyze empirical results.Since the possible solution is vast, construction of an optimal libraryis difficult. Evolving an existing library to match its usage patternpresents a more practical and efficient approach.

It should be recognized that a number of variations of theabove-identified embodiments will be obvious to one of ordinary skill inthe art in view of the foregoing description. Accordingly, the inventionis not to be limited by those specific embodiments and methods of thepresent invention shown and described herein. Rather, the scope of theinvention is to be defined by the following claims and theirequivalents.

What is claimed is:
 1. An optical metrology system that comprises: aplurality of separate optical metrology tools; a shared libraryconnected to said metrology tools and including a series of predictedmeasurement sets, each predicted measurement set corresponding to theempirical measurement sets that the metrology tools would record for avariation of a physical structure; an analysis program configured tosearch the shared library to locate predicted measurement sets thatmatch the empirical measurement sets gathered by the metrology tools,the analysis program also configured to generate a usage pattern for theshared library, the usage pattern identifying the most common searchesperformed on the shared library; and a library evolution tool configuredto add one or more new predicted measurement sets to the library toincrease the number of predicted measurement sets within the sharedlibrary to correspond with the usage pattern so that upon a subsequentsearch of the library the likelihood of locating a predicted measurementset that better matches an empirically obtained measurement set isincreased.
 2. An optical metrology system as recited in claim 1 thatfurther comprises a local library associated with one of the opticalmetrology tools, the local library supplementing the shared library byincluding the predicted measurement sets most used by the associatedoptical metrology tool.
 3. An optical metrology system as recited inclaim 2, wherein the library evolution tool is further configured toreconfigure the local library to correspond with a usage pattern for thelocal library.
 4. A method of analyzing samples with a network includinga first optical metrology tool and a second, separate optical metrologytool, the method comprising the steps of: generating a first locallibrary associated with the first metrology tool and a second, separatelocal library associated with the second metrology tool, each libraryincluding a series of predicted measurement sets, each predictedmeasurement set corresponding to the empirical measurement sets that theassociated metrology tool would record for a variation of a physicalstructure; independently evolving both the first and second locallibraries based on the respective usage of the first and secondmetrology tools; searching the first local library to locate predictedmeasurement sets that match empirical measurement sets gathered by thefirst metrology tool; and reconfiguring the first metrology tool to usethe second local library if the second local library includes predictedmeasurements sets more closely matching the empirical measurement setsgathered by the first metrology tool.